VectorStore Agent: This notebook covers how to interact with VectorStores using an agent.
Python Agent: This notebook covers how to produce and execute python code using an agent.
Pandas DataFrame Agent: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls the Python agent..
CSV Agent: This notebook covers how to do question answering over a csv file. Under the hood this calls the Pandas DataFrame agent.
Agent Types#
The final set of examples are all end-to-end example of different agent types.
In all examples there is an Agent with a particular set of tools.
Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in this documentation. LangChain also provides a list of easily loadable tools. For detailed information on those, please see this documentation
Agents: An agent uses an LLMChain to determine which tools to use. For a list of all available agent types, see here.
MRKL
Tools used: Search, SQLDatabaseChain, LLMMathChain
Agent used: zero-shot-react-description
Paper
Note: This is the most general purpose example, so if you are looking to use an agent with arbitrary tools, please start here.
Example Notebook
Self-Ask-With-Search
Tools used: Search
Agent used: self-ask-with-search
Paper
Example Notebook
ReAct
Tools used: Wikipedia Docstore
Agent used: react-docstore
Paper
Example Notebook
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Key Concepts
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Agents and Vectorstores
 Contents
  
Agent Overview
Agent Toolkits
Agent Types
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Mar 22, 2023.